Abstract
One of the initial steps in the preprocessing of digital fundoscopy images is the identification of pixels containing relevant information. This can be achieved through different approaches, one of them is implementing background extraction, reducing the set of pixels to be analyzed later in the process. In this work, we present a background extraction method for digital fundoscopy images based on computational topology. By interpreting binarized images as cubical complexes and extracting their homological groups in 1 and 2 dimensions we identify a subset of luminescence values that can be used to binarize the original grayscale image, obtaining a mask to achieve background extraction. This method is robust to noise and suboptimal image quality, facilitating the analytical pipeline in the context of computer aided diagnosis approaches. This method facilitates the segmentation of the background of a digital fundoscopy image, which allows further methods to focus on pixels with relevant information (eye fundus). This tool is best suited to be implemented in the preprocessing stages of the analytical pipeline by computational ophthalmology specialists.•It is robust to noise and low-quality images.•Output provides an ideal scenario for down-the-line analysis by facilitating only relevant pixels in a digital fundoscopy.